README.md

metadata

tags:
  - ocr
  - document-processing
  - dots-mocr
  - multilingual
  - markdown
  - uv-script
  - generated
configs:
  - config_name: olmocr-2
    data_files:
      - split: train
        path: olmocr-2/train-*
dataset_info:
  config_name: olmocr-2
  features:
    - name: image
      dtype: image
    - name: b_number
      dtype: string
    - name: page_index
      dtype: int64
    - name: source_row
      dtype: int64
    - name: markdown
      dtype: string
    - name: markdown_metadata
      dtype: string
    - name: inference_info
      dtype: string
  splits:
    - name: train
      num_bytes: 20474496
      num_examples: 50
  download_size: 20342285
  dataset_size: 20474496

Document OCR using dots.mocr

This dataset contains OCR results from images in davanstrien/moh-bench-sample using dots.mocr, a 3B multilingual model with SOTA document parsing and SVG generation.

Processing Details

• Source Dataset: davanstrien/moh-bench-sample
• Model: rednote-hilab/dots.mocr
• Number of Samples: 50
• Processing Time: 11.8 min
• Processing Date: 2026-07-08 16:53 UTC

Configuration

• Image Column: image
• Output Column: markdown
• Dataset Split: train
• Batch Size: 16
• Prompt Mode: ocr
• Max Model Length: 24,000 tokens
• Max Output Tokens: 24,000
• GPU Memory Utilization: 90.0%

Model Information

dots.mocr is a 3B multilingual document parsing model that excels at:

• 100+ Languages — Multilingual document support
• Table extraction — Structured data recognition
• Formulas — Mathematical notation preservation
• Layout-aware — Reading order and structure preservation
• Web screen parsing — Webpage layout analysis
• Scene text spotting — Text detection in natural scenes
• SVG code generation — Charts, UI layouts, scientific figures to SVG

Dataset Structure

The dataset contains all original columns plus:

• markdown: The extracted text in markdown format
• inference_info: JSON list tracking all OCR models applied to this dataset

Usage

from datasets import load_dataset
import json

# Load the dataset
dataset = load_dataset("{output_dataset_id}", split="train")

# Access the markdown text
for example in dataset:
    print(example["markdown"])
    break

# View all OCR models applied to this dataset
inference_info = json.loads(dataset[0]["inference_info"])
for info in inference_info:
    print(f"Column: {info['column_name']} - Model: {info['model_id']}")

Reproduction

This dataset was generated using the uv-scripts/ocr dots.mocr script:

uv run https://huggingface.co/datasets/uv-scripts/ocr/raw/main/dots-mocr.py \
    davanstrien/moh-bench-sample \
    <output-dataset> \
    --image-column image \
    --batch-size 16 \
    --prompt-mode ocr \
    --max-model-len 24000 \
    --max-tokens 24000 \
    --gpu-memory-utilization 0.9

Generated with UV Scripts